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# Deepfake Face Detection Project Details

## 1. Project Overview

This project detects whether a face image is **real** or **fake** using a deep learning image classifier built with **PyTorch** and deployed with **Gradio**.

The project includes:

- a notebook-based training pipeline
- a notebook for descriptive analytics and visualization
- a Gradio app for interactive prediction
- Grad-CAM visualization for interpretability
- saved training artifacts such as metrics, ROC data, and model checkpoint

The current implementation uses a **frozen ResNet18 backbone** with a custom classifier head for binary classification.

---

## 2. Problem Statement

Deepfakes are AI-generated or AI-manipulated media that can look highly realistic. In face-based deepfakes, synthetic images can be used for misinformation, impersonation, fraud, and other forms of misuse.

The aim of this project is to build a system that:

- takes a face image as input
- predicts whether it is **real** or **fake**
- explains the prediction visually using Grad-CAM
- presents performance analytics in a simple interface

---

## 3. Dataset Used

Dataset source:

- Kaggle: `140k Real and Fake Faces`
- URL: <https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces>

### 3.1 Dataset Summary

According to the Kaggle dataset card, the dataset contains:

- `70,000` real face images
- `70,000` fake face images
- total: `140,000` images

The dataset description states that:

- the **real** images come from the Flickr face dataset collected by NVIDIA
- the **fake** images are sampled from a larger set of StyleGAN-generated fake faces
- the images were resized to `256px`
- the dataset was already split into training, validation, and test sets
- CSV files were provided for convenience

### 3.2 Split Used in This Project

This project uses the provided CSV files and folder structure. The current split in the repository is:

| Split | Total | Real | Fake |
|---|---:|---:|---:|
| Train | 100,000 | 50,000 | 50,000 |
| Validation | 20,000 | 10,000 | 10,000 |
| Test | 20,000 | 10,000 | 10,000 |

Approximate percentages:

- Train: `71.43%`
- Validation: `14.29%`
- Test: `14.29%`

### 3.3 Dataset Layout in This Project

```text
deepfakedetection/
|-- train.csv
|-- valid.csv
|-- test.csv
`-- real_vs_fake/
    `-- real-vs-fake/
        |-- train/
        |   |-- fake/
        |   `-- real/
        |-- valid/
        |   |-- fake/
        |   `-- real/
        `-- test/
            |-- fake/
            `-- real/
```

### 3.4 Labels

This project uses:

- `fake -> 0`
- `real -> 1`

---

## 4. High-Level Project Architecture

The overall project flow is:

```text
Dataset
  -> CSV-driven sample loading
  -> Image preprocessing and augmentation
  -> ResNet18-based classifier
  -> Training and validation
  -> Best checkpoint + metrics saved in artifacts/
  -> Gradio app loads best model
  -> User uploads image
  -> Prediction + confidence + Grad-CAM
  -> Analytics charts shown in app
```

### 4.1 Main Components

1. **Dataset Loader**
   Reads image paths and labels from `train.csv`, `valid.csv`, and `test.csv`.

2. **Preprocessing and Augmentation**
   Applies resizing, random crop, horizontal flip, color jitter, normalization, and evaluation transforms.

3. **Model**
   Uses ResNet18 as the feature extractor and a custom fully connected head for binary classification.

4. **Training Pipeline**
   Handles dataloaders, loss, optimizer, scheduler, checkpointing, and early stopping.

5. **Evaluation**
   Computes loss, accuracy, F1-score, ROC-AUC, confusion matrix, and ROC curve points.

6. **Deployment**
   Loads the saved checkpoint in a Gradio app for inference.

7. **Interpretability**
   Uses Grad-CAM to highlight image regions that contributed most to the prediction.

8. **Analytics**
   Displays confusion matrix, ROC curve, and training curves in notebooks and the app.

---

## 5. ResNet Architecture Used

The current model is based on **ResNet18**.

### 5.1 Why ResNet

ResNet is a strong choice for this problem because:

- it is a proven architecture for image classification
- residual connections improve training stability
- transfer learning from ImageNet is straightforward
- it offers a good balance between performance and complexity
- it is easier to explain than heavier or more experimental architectures

### 5.2 ResNet18 Core Structure

ResNet18 mainly uses:

- `7 x 7` convolution in the first layer
- `3 x 3` convolutions in residual blocks
- `1 x 1` convolutions in shortcut/downsampling paths where needed

High-level structure:

1. Input image: `224 x 224 x 3`
2. Initial convolution: `7 x 7`, stride `2`
3. Max-pooling layer
4. Four residual stages with increasing channels:
   - `64`
   - `128`
   - `256`
   - `512`
5. Global average pooling
6. Fully connected classifier head

### 5.3 Classifier Head Used in This Project

The default ImageNet classifier is replaced with a custom head:

```text
in_features -> 256 -> 64 -> 2
```

With activation and regularization:

```text
Dropout
Linear(in_features, 256)
ReLU
Dropout
Linear(256, 64)
ReLU
Dropout
Linear(64, 2)
```

Output classes:

- `fake`
- `real`

### 5.4 Frozen Backbone Strategy

In the current setup:

- the pretrained ResNet18 backbone remains **frozen**
- only the classifier head is trained

This reduces training cost and makes the model more stable for the current project setup.

---

## 6. Training Pipeline

Training is implemented in:

- `deepfake_training.ipynb`

### 6.1 Preprocessing and Augmentation

Training transform:

- resize to slightly larger size
- random resized crop
- random horizontal flip
- light color jitter
- tensor conversion
- ImageNet normalization

Evaluation transform:

- resize to target input size
- tensor conversion
- ImageNet normalization

### 6.2 Current Training Configuration

Current configuration from `artifacts/config.json`:

| Parameter | Value |
|---|---|
| Model | `resnet18` |
| Image size | `224` |
| Batch size | `32` |
| Epochs | `10` |
| Learning rate | `3e-4` |
| Weight decay | `1e-4` |
| Num workers | `0` |
| Patience | `3` |
| Dropout | `0.3` |
| Label smoothing | `0.05` |
| Seed | `42` |
| Freeze backbone epochs | `10` |

### 6.3 Loss Function

The model uses:

- `CrossEntropyLoss`
- with label smoothing

Why:

- the task is binary classification
- the model outputs `2` logits
- CrossEntropyLoss is the standard choice for this setup

### 6.4 Softmax Usage

Softmax is used during evaluation and inference to convert logits into probabilities.

Training does **not** manually apply softmax before loss because `CrossEntropyLoss` handles that internally.

### 6.5 Optimizer and Scheduler

Optimizer:

- `AdamW`

Scheduler:

- `ReduceLROnPlateau`

Purpose:

- `AdamW` helps with stable optimization
- `ReduceLROnPlateau` lowers the learning rate if validation performance stops improving

### 6.6 Early Stopping

The training pipeline uses patience-based early stopping logic.

Current patience:

- `3`

Meaning:

- if validation performance does not improve for `3` consecutive epochs, training can stop early

---

## 7. Evaluation Metrics

The project computes the following metrics:

- loss
- accuracy
- F1-score
- ROC-AUC
- confusion matrix
- classification report
- ROC curve points

### 7.1 Best Validation Metrics

From `artifacts/best_valid_metrics.json`:

- Loss: `0.4169`
- Accuracy: `0.83685`
- F1-score: `0.83678`
- ROC-AUC: `0.91698`

### 7.2 Final Test Metrics

From `artifacts/test_metrics.json`:

- Loss: `0.41783`
- Accuracy: `0.83755`
- F1-score: `0.83801`
- ROC-AUC: `0.91600`

### 7.3 Confusion Matrix

Current test confusion matrix:

```text
[[8347, 1653],
 [1596, 8404]]
```

Interpretation:

- correctly predicted fake: `8347`
- fake predicted as real: `1653`
- real predicted as fake: `1596`
- correctly predicted real: `8404`

These results are much more realistic than the earlier near-perfect runs and suggest better-balanced generalization within this dataset setup.

---

## 8. Artifacts Produced

The training pipeline saves outputs into `artifacts/`.

Current important files:

- `best_model.pt`
- `best_valid_metrics.json`
- `best_valid_roc_curve.json`
- `test_metrics.json`
- `best_model_roc_curve.json`
- `history.json`
- `config.json`

### 8.1 What Each Artifact Contains

- `best_model.pt`
  Best saved model checkpoint

- `best_valid_metrics.json`
  Metrics on the best validation epoch

- `best_valid_roc_curve.json`
  ROC curve points for the best validation checkpoint

- `test_metrics.json`
  Final evaluation metrics on the test set

- `best_model_roc_curve.json`
  ROC curve points for the best saved model on the test split

- `history.json`
  Epoch-wise training and validation history

- `config.json`
  Exact training configuration used for the saved model

---

## 9. Gradio Application

Deployment is implemented in:

- `app.py`

### 9.1 What the App Does

The app:

- loads the saved checkpoint from `artifacts/best_model.pt`
- preprocesses an uploaded image
- predicts whether the image is real or fake
- shows prediction confidence
- generates a Grad-CAM overlay
- renders descriptive analytics charts under the main interface

### 9.2 Current UI Outputs

Main inference section:

- face image input
- prediction summary
- Grad-CAM overlay

Analytics section:

- confusion matrix
- ROC curve
- training curves by epoch

### 9.3 Grad-CAM

Grad-CAM is used to improve interpretability.

Purpose:

- highlights image regions that influenced the model prediction
- helps verify whether the model focuses on face regions instead of irrelevant image areas

Target layer used:

- the last block of `layer4` in ResNet

---

## 10. Descriptive Analytics Notebook

Analytics are implemented in:

- `descriptive_analytics.ipynb`

Current notebook contents:

- dataset split and class balance summary
- best validation and final test metrics summary
- training curves
- confusion matrix
- per-class metrics chart
- ROC curve for the best model

This notebook is meant for:

- project presentation
- analysis of training behavior
- metric visualization
- report preparation

---

## 11. Project File Structure

Important project files:

```text
deepfakedetection/
|-- app.py
|-- deepfake_training.ipynb
|-- descriptive_analytics.ipynb
|-- requirements.txt
|-- README.md
|-- train.csv
|-- valid.csv
|-- test.csv
|-- artifacts/
|   |-- best_model.pt
|   |-- best_valid_metrics.json
|   |-- best_valid_roc_curve.json
|   |-- test_metrics.json
|   |-- best_model_roc_curve.json
|   |-- history.json
|   `-- config.json
|-- imgForDemo/
|   |-- fake/
|   `-- real/
`-- real_vs_fake/
    `-- real-vs-fake/
        |-- train/
        |-- valid/
        `-- test/
```

---

## 12. Requirements

From `requirements.txt`, the current Python dependencies are:

- `torch`
- `torchvision`
- `gradio`
- `huggingface_hub`
- `Pillow`
- `tqdm`
- `numpy`
- `matplotlib`
- `jupyter`

### 12.1 Software Requirements

- Python
- Jupyter Notebook
- PyTorch
- Torchvision
- Gradio
- NumPy
- Matplotlib
- PIL / Pillow

### 12.2 Hardware Requirements

Recommended:

- GPU-enabled system for training
- sufficient disk space for the dataset and artifacts

GPU used in this project:

- `NVIDIA GeForce RTX 4050 Laptop GPU`

---

## 13. How to Run the Project

### 13.1 Training

Open:

- `deepfake_training.ipynb`

Run the notebook cells from top to bottom.

### 13.2 Analytics

Open:

- `descriptive_analytics.ipynb`

Run the cells to view charts and summaries.

### 13.3 Gradio App

Run:

```powershell
python app.py --checkpoint artifacts/best_model.pt
```

Then open:

```text
http://127.0.0.1:7860
```

---

## 14. Current Strengths of the Project

- clean notebook-based training workflow
- balanced dataset split
- reproducible saved artifacts
- Grad-CAM-based interpretability
- browser-based deployment with Gradio
- integrated analytics for presentation and reporting
- realistic evaluation metrics after reducing over-optimistic earlier behavior

---

## 15. Current Limitations

- the current model uses only a frozen ResNet18 backbone
- generalization to completely different deepfake datasets is not yet tested
- the project focuses on image-level deepfake detection, not video-level detection
- no face-crop specific preprocessing pipeline is currently used
- the dataset may still contain dataset-specific shortcuts not fully removed

---

## 16. Possible Future Improvements

- partially unfreeze the backbone for fine-tuning
- compare ResNet18 with ResNet34 or ResNet50
- add face detection and train on cropped faces only
- evaluate on another external deepfake dataset
- add multiple checkpoint selection in the app
- extend from image-based detection to video deepfake detection
- add confidence calibration and deeper bias analysis

---

## 17. Source and Attribution

Dataset used:

- Kaggle dataset page: <https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces>

Dataset description used for this document was based on the Kaggle dataset card, which states that the dataset contains `70k` real faces and `70k` StyleGAN-generated fake faces, resized and split into train, validation, and test sets with CSV support.

	# Deepfake Face Detection Project Details

	## 1. Project Overview

	This project detects whether a face image is real or fake using a deep learning image classifier built with PyTorch and deployed with Gradio.

	The project includes:

	- a notebook-based training pipeline
	- a notebook for descriptive analytics and visualization
	- a Gradio app for interactive prediction
	- Grad-CAM visualization for interpretability
	- saved training artifacts such as metrics, ROC data, and model checkpoint

	The current implementation uses a frozen ResNet18 backbone with a custom classifier head for binary classification.

	---

	## 2. Problem Statement

	Deepfakes are AI-generated or AI-manipulated media that can look highly realistic. In face-based deepfakes, synthetic images can be used for misinformation, impersonation, fraud, and other forms of misuse.

	The aim of this project is to build a system that:

	- takes a face image as input
	- predicts whether it is real or fake
	- explains the prediction visually using Grad-CAM
	- presents performance analytics in a simple interface

	---

	## 3. Dataset Used

	Dataset source:

	- Kaggle: `140k Real and Fake Faces`
	- URL: <https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces>

	### 3.1 Dataset Summary

	According to the Kaggle dataset card, the dataset contains:

	- `70,000` real face images
	- `70,000` fake face images
	- total: `140,000` images

	The dataset description states that:

	- the real images come from the Flickr face dataset collected by NVIDIA
	- the fake images are sampled from a larger set of StyleGAN-generated fake faces
	- the images were resized to `256px`
	- the dataset was already split into training, validation, and test sets
	- CSV files were provided for convenience

	### 3.2 Split Used in This Project

	This project uses the provided CSV files and folder structure. The current split in the repository is:

	\| Split \| Total \| Real \| Fake \|
	\|---\|---:\|---:\|---:\|
	\| Train \| 100,000 \| 50,000 \| 50,000 \|
	\| Validation \| 20,000 \| 10,000 \| 10,000 \|
	\| Test \| 20,000 \| 10,000 \| 10,000 \|

	Approximate percentages:

	- Train: `71.43%`
	- Validation: `14.29%`
	- Test: `14.29%`

	### 3.3 Dataset Layout in This Project

	```text
	deepfakedetection/
	\|-- train.csv
	\|-- valid.csv
	\|-- test.csv
	`-- real_vs_fake/
	`-- real-vs-fake/
	\|-- train/
	\| \|-- fake/
	\| `-- real/
	\|-- valid/
	\| \|-- fake/
	\| `-- real/
	`-- test/
	\|-- fake/
	`-- real/
	```

	### 3.4 Labels

	This project uses:

	- `fake -> 0`
	- `real -> 1`

	---

	## 4. High-Level Project Architecture

	The overall project flow is:

	```text
	Dataset
	-> CSV-driven sample loading
	-> Image preprocessing and augmentation
	-> ResNet18-based classifier
	-> Training and validation
	-> Best checkpoint + metrics saved in artifacts/
	-> Gradio app loads best model
	-> User uploads image
	-> Prediction + confidence + Grad-CAM
	-> Analytics charts shown in app
	```

	### 4.1 Main Components

	1. Dataset Loader
	Reads image paths and labels from `train.csv`, `valid.csv`, and `test.csv`.

	2. Preprocessing and Augmentation
	Applies resizing, random crop, horizontal flip, color jitter, normalization, and evaluation transforms.

	3. Model
	Uses ResNet18 as the feature extractor and a custom fully connected head for binary classification.

	4. Training Pipeline
	Handles dataloaders, loss, optimizer, scheduler, checkpointing, and early stopping.

	5. Evaluation
	Computes loss, accuracy, F1-score, ROC-AUC, confusion matrix, and ROC curve points.

	6. Deployment
	Loads the saved checkpoint in a Gradio app for inference.

	7. Interpretability
	Uses Grad-CAM to highlight image regions that contributed most to the prediction.

	8. Analytics
	Displays confusion matrix, ROC curve, and training curves in notebooks and the app.

	---

	## 5. ResNet Architecture Used

	The current model is based on ResNet18.

	### 5.1 Why ResNet

	ResNet is a strong choice for this problem because:

	- it is a proven architecture for image classification
	- residual connections improve training stability
	- transfer learning from ImageNet is straightforward
	- it offers a good balance between performance and complexity
	- it is easier to explain than heavier or more experimental architectures

	### 5.2 ResNet18 Core Structure

	ResNet18 mainly uses:

	- `7 x 7` convolution in the first layer
	- `3 x 3` convolutions in residual blocks
	- `1 x 1` convolutions in shortcut/downsampling paths where needed

	High-level structure:

	1. Input image: `224 x 224 x 3`
	2. Initial convolution: `7 x 7`, stride `2`
	3. Max-pooling layer
	4. Four residual stages with increasing channels:
	- `64`
	- `128`
	- `256`
	- `512`
	5. Global average pooling
	6. Fully connected classifier head

	### 5.3 Classifier Head Used in This Project

	The default ImageNet classifier is replaced with a custom head:

	```text
	in_features -> 256 -> 64 -> 2
	```

	With activation and regularization:

	```text
	Dropout
	Linear(in_features, 256)
	ReLU
	Dropout
	Linear(256, 64)
	ReLU
	Dropout
	Linear(64, 2)
	```

	Output classes:

	- `fake`
	- `real`

	### 5.4 Frozen Backbone Strategy

	In the current setup:

	- the pretrained ResNet18 backbone remains frozen
	- only the classifier head is trained

	This reduces training cost and makes the model more stable for the current project setup.

	---

	## 6. Training Pipeline

	Training is implemented in:

	- `deepfake_training.ipynb`

	### 6.1 Preprocessing and Augmentation

	Training transform:

	- resize to slightly larger size
	- random resized crop
	- random horizontal flip
	- light color jitter
	- tensor conversion
	- ImageNet normalization

	Evaluation transform:

	- resize to target input size
	- tensor conversion
	- ImageNet normalization

	### 6.2 Current Training Configuration

	Current configuration from `artifacts/config.json`:

	\| Parameter \| Value \|
	\|---\|---\|
	\| Model \| `resnet18` \|
	\| Image size \| `224` \|
	\| Batch size \| `32` \|
	\| Epochs \| `10` \|
	\| Learning rate \| `3e-4` \|
	\| Weight decay \| `1e-4` \|
	\| Num workers \| `0` \|
	\| Patience \| `3` \|
	\| Dropout \| `0.3` \|
	\| Label smoothing \| `0.05` \|
	\| Seed \| `42` \|
	\| Freeze backbone epochs \| `10` \|

	### 6.3 Loss Function

	The model uses:

	- `CrossEntropyLoss`
	- with label smoothing

	Why:

	- the task is binary classification
	- the model outputs `2` logits
	- CrossEntropyLoss is the standard choice for this setup

	### 6.4 Softmax Usage

	Softmax is used during evaluation and inference to convert logits into probabilities.

	Training does not manually apply softmax before loss because `CrossEntropyLoss` handles that internally.

	### 6.5 Optimizer and Scheduler

	Optimizer:

	- `AdamW`

	Scheduler:

	- `ReduceLROnPlateau`

	Purpose:

	- `AdamW` helps with stable optimization
	- `ReduceLROnPlateau` lowers the learning rate if validation performance stops improving

	### 6.6 Early Stopping

	The training pipeline uses patience-based early stopping logic.

	Current patience:

	- `3`

	Meaning:

	- if validation performance does not improve for `3` consecutive epochs, training can stop early

	---

	## 7. Evaluation Metrics

	The project computes the following metrics:

	- loss
	- accuracy
	- F1-score
	- ROC-AUC
	- confusion matrix
	- classification report
	- ROC curve points

	### 7.1 Best Validation Metrics

	From `artifacts/best_valid_metrics.json`:

	- Loss: `0.4169`
	- Accuracy: `0.83685`
	- F1-score: `0.83678`
	- ROC-AUC: `0.91698`

	### 7.2 Final Test Metrics

	From `artifacts/test_metrics.json`:

	- Loss: `0.41783`
	- Accuracy: `0.83755`
	- F1-score: `0.83801`
	- ROC-AUC: `0.91600`

	### 7.3 Confusion Matrix

	Current test confusion matrix:

	```text
	[[8347, 1653],
	[1596, 8404]]
	```

	Interpretation:

	- correctly predicted fake: `8347`
	- fake predicted as real: `1653`
	- real predicted as fake: `1596`
	- correctly predicted real: `8404`

	These results are much more realistic than the earlier near-perfect runs and suggest better-balanced generalization within this dataset setup.

	---

	## 8. Artifacts Produced

	The training pipeline saves outputs into `artifacts/`.

	Current important files:

	- `best_model.pt`
	- `best_valid_metrics.json`
	- `best_valid_roc_curve.json`
	- `test_metrics.json`
	- `best_model_roc_curve.json`
	- `history.json`
	- `config.json`

	### 8.1 What Each Artifact Contains

	- `best_model.pt`
	Best saved model checkpoint

	- `best_valid_metrics.json`
	Metrics on the best validation epoch

	- `best_valid_roc_curve.json`
	ROC curve points for the best validation checkpoint

	- `test_metrics.json`
	Final evaluation metrics on the test set

	- `best_model_roc_curve.json`
	ROC curve points for the best saved model on the test split

	- `history.json`
	Epoch-wise training and validation history

	- `config.json`
	Exact training configuration used for the saved model

	---

	## 9. Gradio Application

	Deployment is implemented in:

	- `app.py`

	### 9.1 What the App Does

	The app:

	- loads the saved checkpoint from `artifacts/best_model.pt`
	- preprocesses an uploaded image
	- predicts whether the image is real or fake
	- shows prediction confidence
	- generates a Grad-CAM overlay
	- renders descriptive analytics charts under the main interface

	### 9.2 Current UI Outputs

	Main inference section:

	- face image input
	- prediction summary
	- Grad-CAM overlay

	Analytics section:

	- confusion matrix
	- ROC curve
	- training curves by epoch

	### 9.3 Grad-CAM

	Grad-CAM is used to improve interpretability.

	Purpose:

	- highlights image regions that influenced the model prediction
	- helps verify whether the model focuses on face regions instead of irrelevant image areas

	Target layer used:

	- the last block of `layer4` in ResNet

	---

	## 10. Descriptive Analytics Notebook

	Analytics are implemented in:

	- `descriptive_analytics.ipynb`

	Current notebook contents:

	- dataset split and class balance summary
	- best validation and final test metrics summary
	- training curves
	- confusion matrix
	- per-class metrics chart
	- ROC curve for the best model

	This notebook is meant for:

	- project presentation
	- analysis of training behavior
	- metric visualization
	- report preparation

	---

	## 11. Project File Structure

	Important project files:

	```text
	deepfakedetection/
	\|-- app.py
	\|-- deepfake_training.ipynb
	\|-- descriptive_analytics.ipynb
	\|-- requirements.txt
	\|-- README.md
	\|-- train.csv
	\|-- valid.csv
	\|-- test.csv
	\|-- artifacts/
	\| \|-- best_model.pt
	\| \|-- best_valid_metrics.json
	\| \|-- best_valid_roc_curve.json
	\| \|-- test_metrics.json
	\| \|-- best_model_roc_curve.json
	\| \|-- history.json
	\| `-- config.json
	\|-- imgForDemo/
	\| \|-- fake/
	\| `-- real/
	`-- real_vs_fake/
	`-- real-vs-fake/
	\|-- train/
	\|-- valid/
	`-- test/
	```

	---

	## 12. Requirements

	From `requirements.txt`, the current Python dependencies are:

	- `torch`
	- `torchvision`
	- `gradio`
	- `huggingface_hub`
	- `Pillow`
	- `tqdm`
	- `numpy`
	- `matplotlib`
	- `jupyter`

	### 12.1 Software Requirements

	- Python
	- Jupyter Notebook
	- PyTorch
	- Torchvision
	- Gradio
	- NumPy
	- Matplotlib
	- PIL / Pillow

	### 12.2 Hardware Requirements

	Recommended:

	- GPU-enabled system for training
	- sufficient disk space for the dataset and artifacts

	GPU used in this project:

	- `NVIDIA GeForce RTX 4050 Laptop GPU`

	---

	## 13. How to Run the Project

	### 13.1 Training

	Open:

	- `deepfake_training.ipynb`

	Run the notebook cells from top to bottom.

	### 13.2 Analytics

	Open:

	- `descriptive_analytics.ipynb`

	Run the cells to view charts and summaries.

	### 13.3 Gradio App

	Run:

	```powershell
	python app.py --checkpoint artifacts/best_model.pt
	```

	Then open:

	```text
	http://127.0.0.1:7860
	```

	---

	## 14. Current Strengths of the Project

	- clean notebook-based training workflow
	- balanced dataset split
	- reproducible saved artifacts
	- Grad-CAM-based interpretability
	- browser-based deployment with Gradio
	- integrated analytics for presentation and reporting
	- realistic evaluation metrics after reducing over-optimistic earlier behavior

	---

	## 15. Current Limitations

	- the current model uses only a frozen ResNet18 backbone
	- generalization to completely different deepfake datasets is not yet tested
	- the project focuses on image-level deepfake detection, not video-level detection
	- no face-crop specific preprocessing pipeline is currently used
	- the dataset may still contain dataset-specific shortcuts not fully removed

	---

	## 16. Possible Future Improvements

	- partially unfreeze the backbone for fine-tuning
	- compare ResNet18 with ResNet34 or ResNet50
	- add face detection and train on cropped faces only
	- evaluate on another external deepfake dataset
	- add multiple checkpoint selection in the app
	- extend from image-based detection to video deepfake detection
	- add confidence calibration and deeper bias analysis

	---

	## 17. Source and Attribution

	Dataset used:

	- Kaggle dataset page: <https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces>

	Dataset description used for this document was based on the Kaggle dataset card, which states that the dataset contains `70k` real faces and `70k` StyleGAN-generated fake faces, resized and split into train, validation, and test sets with CSV support.